metaAnalysisESpath: Performing Gene Set Enrichment Meta-analysis

In GSEMA: Gene Set Enrichment Meta-Analysis

Performing Gene Set Enrichment Meta-analysis

Description

It performs Gene Sets Enrichment meta-analysis by applying Effects size combination methods

Usage

metaAnalysisESpath(
  objectMApath = NULL,
  effectS = NULL,
  measure = c("limma", "SMD", "MD"),
  WithinVarCorrect = TRUE,
  typeMethod = c("REM", "FEM"),
  missAllow = 0.3,
  numData = length(objectMApath)
)

Arguments

objectMApath	A list of list. Each list contains two elements. The first element is the Gene Set matrix (gene sets in rows and samples in columns) and the second element is a vector of zeros and ones that represents the state of the different samples of the Gene Sets matrix. 0 represents one group (controls) and 1 represents the other group (cases).
effectS	A list of two elements. The first element is a dataframe with gene sets in rows and studies in columns. Each component of the dataframe is the effect of a gene set in a study. The second element of the list is also a dataframe with the same structure, but in this case each component of the dataframe represent the variance of the effect of a gene set in a study. This argument should be only used in the case that objectMApath argument is null.
measure	A character string that indicates the type of effect size to be calculated. The options are "limma", "SMD" and "MD". The default value is "limma". See details for more information.
WithinVarCorrect	A logical value that indicates if the within variance correction should be applied. The default value is TRUE. See details for more information.
typeMethod	A character that indicates the method to be performed. See "Details"for more information
missAllow	a number that indicates the maximum proportion of missing values allowed in a sample. If the sample has more proportion of missing values the sample will be eliminated. In the other case the missing values will be imputed using the K-NN algorithm.
numData	The minimum number of datasets in which a gene must be contained to be included in the emta-analysis. By default, the gene must be contained in all the datasets. If the number entered exceeds the number of studies, the total number of studies will be considered."

Details

There are different ways to calculate the effect size of a gene set:

1. "MD": Raw Mean Difference (Borenstein, 2009)

2. "SMD": Standardized Mean Difference (Hedges, 1981)

3. "limma": Standardized Mean Difference calculated from the t-statistics and degrees of freedom obtained by the limma package by applying the transformation of Rosenthal and Rosnow, 2008). Its calculation is similar to the one proposed by (Marot et al., 2009) but considering the transformation of (Rosenthal and Rosnow, 2008).

The correction of the variance of the effect size is based on Lin L, Aloe AM (2021) in which the variance is calculated from the different estimators.

The meta-analysis methods that can be applied are:

1. "FEM": Fixed Effects model

2. "REM": Random Effects model (Default).

Value

A dataframe with the meta-analysis results. For more information see the package vignette.

Author(s)

Juan Antonio Villatoro Garcia, [e-mail]

References

Toro-Domínguez D., Villatoro-García J.A., Martorell-Marugán J., Román-Montoya Y., Alarcón-Riquelme M.E., Carmona-Sáez P. (2020). A survey of gene expression meta-analysis: methods and applications, Briefings in Bioinformatics, bbaa019, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bib/bbaa019")}

Borenstein, M. (2009). Effect sizes for continuous data. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta-analysis (2nd ed., pp. 221–235). New York: Russell Sage Foundation.

Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.2307/1164588")}

Lin L, Aloe AM (2021). Evaluation of various estimators for standardized mean difference in meta-analysis. Stat Med. 2021 Jan 30;40(2):403-426. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.8781")}

Marot, G., Foulley, J. L., Mayer, C. D., & Jaffrézic, F. (2009). Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics. 2692-2699. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btp444")}

Rosenthal, R., & Rosnow, R. L. (2008). Essentials of behavioral research: Methods and data analysis. McGraw-Hill.

See Also

calculateESpath

Examples

data("simulatedData")
results <- metaAnalysisESpath(objectMApath = objectMApathSim,
    measure = "limma", typeMethod = "REM")

GSEMA documentation built on Oct. 14, 2024, 5:09 p.m.